{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport cv2\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout, Activation, Conv2D, MaxPooling2D, BatchNormalization\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n\nimport zipfile\n\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"main_dir = \"/kaggle/working/\"\ntrain_dir = \"train\"\npath = os.path.join(main_dir,train_dir)\n\nX = []\ny = []\nconvert = lambda category : int(category == 'dog')\ndef create_dataset(path):\n    for p in os.listdir(path):\n        category = p.split(\".\")[0]\n        category = convert(category)\n        img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n        new_img_array = cv2.resize(img_array, dsize=(80, 80))\n        X.append(new_img_array)\n        y.append(category)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"create_dataset(path)\nX = np.array(X).reshape(-1, 80,80,1)\ny = np.array(y)\nX = X/255.0\nprint(X.shape)\nprint(y.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(64,(3,3), activation = 'relu', input_shape = X.shape[1:]))\nmodel.add(MaxPooling2D(pool_size = (2,2)))\nmodel.add(Dropout(0.5))\nmodel.add(Conv2D(64,(3,3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size = (2,2)))\nmodel.add(Dropout(0.5))\nmodel.add(Flatten())\nmodel.add(Dense(512, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(1, activation='sigmoid'))\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X, y, epochs=20, batch_size=128, validation_split=0.2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = np.array(X).reshape(-1, 6400)\ny_train = np.array(y)\nprint(X_train.shape)\nprint(y_train.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_2 = Sequential()\nmodel_2.add(Dense(4096, activation='relu', input_shape = X_train.shape[1:]))\nmodel_2.add(Dropout(0.5))\nmodel_2.add(Dense(2048, activation='relu'))\nmodel_2.add(Dropout(0.5))\nmodel_2.add(BatchNormalization())\nmodel_2.add(Dense(512,activation='relu'))\nmodel_2.add(Dropout(0.5))\nmodel_2.add(Dense(256, activation='relu'))\nmodel_2.add(Dropout(0.5))\nmodel_2.add(BatchNormalization())\nmodel_2.add(Dense(64, activation='relu'))\nmodel_2.add(Dense(1,activation='sigmoid'))\nmodel_2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_2.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_2.fit(X_train, y_train, epochs=30, batch_size=128, validation_split=0.2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}